In the current era of web-scale datasets, high throughput biology, and
multilanguage machine translation, modern datasets no longer fit on a
single computer and traditional machine learning algorithms often have
prohibitively long running times. Parallel and distributed machine
learning is no longer a luxury; it has become a necessity. Moreover,
industry leaders have already declared that clouds are the future of
computing, and new computing platforms such as Microsoft's Azure and
Amazon's EC2 are bringing distributed computing to the masses.

The machine learning community is reacting to this trend in computing
by developing new parallel and distributed machine learning
techniques. However, many important challenges remain
unaddressed. Practical distributed learning algorithms must deal with
limited network resources, node failures and nonuniform network
latencies. In cloud environments, where network latencies are
especially large, distributed learning algorithms should take
advantage of asynchronous updates.

Many similar issues have been addressed in other fields, where
distributed computation is more mature, such as convex optimization
and numerical computation. We can learn from their successes and their
failures.

The one day workshop on "Learning on Cores, Clusters, and Clouds" aims
to bring together experts in the field and curious newcomers, to
present the state-of-the-art in applied and theoretical distributed
learning, and to map out the challenges ahead. The workshop will
include invited and contributed presentations from leaders in
distributed learning and adjacent fields.

We would like to invite short high-quality submissions on the
following topics:

Distributed algorithms for online and batch learning

Parallel (multicore) algorithms for online and batch learning

Computational models and theoretical analysis of distributed and
parallel learning

Communication avoiding algorithms

Learning algorithms that are robust to hardware failures

Experimental results and interesting applications

Interesting submissions in other relevant topics not listed above are
welcome too. Due to the time constraints, most accepted submissions
will be presented as poster spotlights.

Submission guidelines:

Submissions should be written as extended abstracts, no longer than 4
pages in the NIPS latex style. NIPS style files and formatting
instructions can be found at
http://nips.cc/PaperInformation/StyleFiles. The submissions should
include the authors' name and affiliation since the review
process will not be double blind. The extended abstract
may be accompanied by an unlimited appendix and other supplementary
material, with the understanding that anything beyond 4 pages may be
ignored by the program committee. Please send your submission by email
to submit.lccc@gmail.com
before October 17 at midnight
PST. Notifications will be given on or before Nov 7. Topics that were
recently published or presented elsewhere are allowed, provided that
the extended abstract mentions this explicitly; topics that were
presented in non-machine-learning conferences are especially
encouraged.